Augmenting this project https://gist.github.com/bartolli/5291b7dd4940b04903cae6141303b50d 1. Layered Node Construction: - Abstract Nodes by Semantics: Instead of directly translating SQL tables 1-to-1 into graph nodes, group data into abstract layers or "meta-nodes" based on semantics. For instance, create entity types that combine multiple tables or columns sharing a common meaning or relationship. This would allow pruning unnecessary nodes at higher abstraction layers. Example: Combine multiple address-related tables into a single Address node, abstracting away unnecessary details if higher-level queries only need to reference the user-location relationship. - Typed Relationships: Consider the nuances of relationships—some relationships are transient, others may be hierarchical. Use relationship types that reflect the depth of the relationship. This allows the query engine to prune relationships irrelevant to the query based on their type (e.g., PART_OF, HAS_SUBSET, LINKS_TO). 2. Hierarchical Indexing for Pruning: - Hierarchical Node IDs: Structure node IDs to represent hierarchical layers in the data. For example, if you have user-related data, node IDs can reflect levels like Country -> City -> User. This way, queries that don't need fine-grained data can prune branches by querying only higher levels. Example: A query on users from New York might only scan the City layer without diving into individual user nodes. - Indexing Relationships by Depth: Similarly, you could add indexing or scoring based on relationship "depth" or importance. Queries that don't need to evaluate deep relationships can prune the unnecessary links. 3. Progressive Detail Unfolding (Lazy Loading): - On-Demand Node Construction: Rather than constructing all nodes upfront, use lazy loading techniques to unfold data only when needed. This allows the graph engine to prune nodes that don’t affect the result, based on query patterns. Example: For a social network, initially load only the high-level User and City nodes, while friend connections (FRIENDS_WITH) are loaded only if explicitly needed. - Heuristic Node Pruning: Introduce heuristics that rank nodes based on relevance (e.g., frequency of access, importance in the hierarchy, etc.). If a node is rarely accessed, mark it as "prunable" for queries not requiring full details. This is useful for frequently queried datasets where only a subset of the graph is commonly accessed.